It is known to equalize distorted data signals by means of linear filters.
The data signal recorded on a magnetic tape is shown in FIG. 1.
FIG. 2 shows the playback signal read from the magnetic tape.
It is clearly to be seen that the steepness of the edges in strongly distorted. An already mentioned, linear filters are provided for equalizing data signals. However, given rising data densities it is increasingly becoming more difficult to realize the equalization of magnetically recorded data signals by means of linear filters.
It is therefore the object of the invention substantially to improve the equalization of distorted data signals, in particular magnetically recorded data signals.
The invention achieves this object by providing a neural network for equalizing distorted data signals.
The advantages of a neural network instead of linear filters in the equalization of distorted data signals is now explained using the example of a magnetically recorded data signal.
The invention proceeds from the finding that in the case of a magnetic tape channel it is less a question of a causal transmission system, as is desirable for a linear filter design, than of an acausal system.
The recording and later replaying of data represents a two-stage procedure. Owing to a non-punctiform magnetization and to the stray fields, which can far exceed the wavelengths of the high signal frequencies, when recording an effect is achieved not only at the band position which is just current, but also in the case of events situated previously in time, that is to say in the came of already recorded events. The result of this is that during playback an event which is just current is influenced by successive "future" events, so that it is no longer possible to speak of a causal transmission system in the true sense. The temporal sequence of cause and effect no longer obtains. It is clearly to be seen in FIG. 2 that the playback signal is already rising clearly before the edge to be seen in the record signal risen. The use of linear filters in acausal systems is, however, restricted and leads to an unsatisfactory equalization of distorted data signals. The use of all-pass filters, which can be set only with difficulty, is frequently required.
The invention proceeds from the idea of regarding the playback signal as actual value and the digital record signal as desired value. The neural network does not carry out filtering, but compares the signal pattern of the record signal with signal patterns of the playback signal. Instead of filtering signals by means of linear filters, the invention compares signal patterns.
The theory of neural networks is based on findings which were obtained in the biological and neurological research from investigations on nerve fibers. The conversion of these findings into technology goes back in wide fields to the work of D. O. Habb in 1961. The following three features are essentially concerned with here: neurons communicate with one another over so-called synapses, which are to be regarded as switching points via which a signal of a specific intensity in led to the centre of a cell.
All incoming weighted signals are summed in the cell and a non-linear activation function is applied to them in order to generate the output signal of the neuron which, in turn, is relayed via synapses to other neurons. The essential characteristic of the nerve cells in the brain is their learning ability. It is assumed that the learning process in performed by setting the weightings between the neurons.
These three quoted characteristics of a biological neural network can be transferred to digital and analog electric circuits.